Nanoscale materials with unique mechanical, electronic, optical and chemical properties have a variety of potential applications such as nanoelectromecahnical systems (NEMS) and nanosensors. The development of nano-assembly technologies will lead to potential breakthroughs in manufacturing new revolutionary industrial products. The techniques for nano-assembly can be generally classified into bottom-up and top-down methods. Self-assembly in nanoscale is reported as the most promising bottom-up technique, which is applied to make, regular, symmetric patterns of nanoentites. However, many potential nanostructures and nanodevices are asymmetric, which cannot be manufactured using self-assembly only. A top-down method would be desirable to fabricate complex nanostructures.
The semiconductor fabrication technique is a matured “top-down” method, which has been used in the fabrication of microelectromechanical systems (MEMS). However, it is difficult to build nano-structures using this method due to limitations of the lithography in which the smallest feature that can be made must be larger than half the wavelength of the light used in the lithography. Although smaller features can be made by electron beam (e-beam) nanolithography, it is practically very difficult to position the feature precisely using e-beam nanolithography. The high cost of the SEM, ultrahigh vacuum condition, and space limitation inside the SEM vacuum capsule also impede its wide applications.
Atomic force microscopy has been proven to be a powerful technique to study sample surfaces down to the nanoscale. It can work with both conductive and insulating materials and in many conditions, such as air and liquid. Not only can it characterize sample surfaces, it can also modify them through nanolithography and nanomanipulation which are promising nano-fabrication techniques that combine “top-down” and “bottom-up” advantages. In recent years, many kinds of AFM-based nanolithography have been implemented on a variety of surfaces such as semiconductors, metals and soft materials and a variety of AFM-based nanomanipulation schemes have been developed to position and manipulate nanoobjects. However, nanolithography itself can hardly be considered as sufficient for fabrication of a complete device. Thus, manipulation of nanoobjects has to be involved in order to manufacture nanostructures and nanodevices. The AFM-based nanomanipulation is much more complicated and difficult than the AFM-based nanolithography because nanoobjects have to be manipulated from one place to another by the AFM tip and some times it is necessary to relocate the nanoobjects during nanomanipulation while only patterns will be drawn during nanolithography. Since the AFM tip as the manipulation end-effector can only apply a point force on a nanoobject, the pushing point on the nanoobject has to be precisely controlled in order to manipulate the nanoobjects to their desired positions. But positioning errors due to the random drift and the deformation of the cantilever cause the nanoobjects to be easily lost during manipulation or manipulated to wrong places. In the most recently available AFM-based manipulation method, the manipulation paths are obtained either manually using haptic devices or in an interactive way between the users and the atomic force microscope (AFM) images. The main problem of these schemes is their lack of real-time visual feedback. Since the nanoobjects can be easily lost or manipulated to wrong destinations during manipulation using these schemes, the result of each operation has to be verified by a new image scan before the next operation starts. This scan-design-manipulation-scan cycle is usually time consuming and inefficient.
In order to increase the efficiency and accuracy of AFM-based nano-assembly, automated CAD guided nano-assembly is desirable. In the macro-world, CAD guided automated manufacturing has been widely studied. However, it is not a trivial extension from the macro-world to the nanoworld. In the nanoenvironments, the nanoobjects, which include nanoparticles, nanorods, nanowires, nanotubes and etc., are usually distributed on a substrate surface randomly. Therefore, the nanoenvironment and the available nanoobjects have to be modeled in order to design a feasible nanostructure. Because manipulation of nanoparticles only requires translation while manipulation of other nanoobjects such as nanorods involves both translation and rotation, manipulation of nanorods is more challenging than that of nanoparticles. To generate a feasible path to manipulate nanoobjects, obstacle avoidance has to be considered too. Turns around obstacles should also be avoided since turns may cause the failure of the manipulation. Because of the positioning errors due to the random drift, the actual position of each nanoobject has to be identified before each operation. Therefore, it is desirable to develop an automated nanomanipulation system which addresses these and other concerns.